Object detection for a single object only

Question

I have been working with object detection. But these methods consist of very deep neural networks and require lots of memory to store the trained models. E.g. I once tried to train a Mask R-CNN model, and the weights take 200 MB.

However, my focus is on detecting a single object only. So, I guess these methods are not suitable. Are there any object detection method that can do this job with a low memory requirement?

Answer 1

You can try SSD or faster RCNN they are easily available in Tensorflow object detection API

https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

here you can get pre-trained models and config file you can select your model by taking look on speed and mAP(accuracy) column as per your requirement.

Answer 2

Following mukul's answer, I specifically recommend you check out SSDLite-MobileNetV2. It's a lite-weight model, which is still enough expressive for good results. Especially when you're restricting yourself to a single class, as you can see in the example of FaceSSD-MobileNetV2 as in here (Note however this is vanilla SSD). So you can simply Take the pre-trained model of SSDLite-MobileNetV2 with the corresponding config file, and modify it for a single class. This means changing num_classes to 1, modifying the label_map.pbtxt, and of course - preparing the dataset with the single class you want.

If you want a more robust model, but which has no pre-trained mode, you can use an FPN version. Checkout this config file, which is with MobileNetV1, and modify it for your needs (e.g. switching to MobileNetV2, switching to use_depthwise, etc). On one hand, there's no detection pre-trained model, but on the other the detection head is shared over all (relevant) scales, so it's somewhat easier to train. So simply fine-tune it from the corresponding classification checkpoint from here.